Table REF  provides the performance obtained by different transformer models. Most transformer models obtained an averaged-weighted F1-score of higher than 0.83 for binary and higher than 0.70 for multiclass hope speech detection, which illustrates the strength of transformers over the rest of the learning models. For the binary classification task, models using BERT, Roberta, and XLNet obtained the highest results with averaged-macro F1-scores of 0.85. On the other hand, for the multiclass task BERT [1]} model outperformed the rest of the transformers with averaged-weighted and averaged-macro F1-scores of 0.77 and 0.72, respectively.
A comparison of the performances of the different learning approaches shows remarkable differences between the transformers and other learning models.
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